Donghang Shao


DOI bib
Reducing the Statistical Distribution Error in Gridded Precipitation Data for the Tibetan Plateau
Jiapei Ma, Hongyi Li, Jian Wang, Xiaohua Hao, Donghang Shao, Haike Lei
Journal of Hydrometeorology, Volume 21, Issue 11

Abstract Gridded precipitation data are very important for hydrological and meteorological studies. However, gridded precipitation can exhibit significant statistical bias that needs to be corrected before application, especially in regions where high wind speeds, frequent snowfall, and sparse observation networks can induce significant uncertainties in the final gridded datasets. In this paper, we present a method for the production of gridded precipitation on the Tibetan Plateau (TP). This method reduces the statistical distribution error by correcting for wind-induced undercatch and optimizing the interpolation method. A gridded precipitation product constructed by this method was compared with previous products on the TP. The results show that undercatch correction is necessary for station data, which can reduce the distributional error by 30% at most. A thin-plate splines interpolation algorithm considering altitude as a covariate is helpful to reduce the statistical distributional error in general. Our method effectively inhibits the smoothing effect in gridded precipitation, and compared to previous products, results in a higher mean value, larger 98th percentile, and greater temporal variance. This study can help to improve the quality of gridded precipitation over the TP.

DOI bib
Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations
Donghang Shao, Wenbo Xu, Hongyi Li, Wang Jian, Xiaohua Hao
Remote Sensing, Volume 12, Issue 18

Snow surface spectral reflectance is very important in the Earth’s climate system. Traditional land surface models with parameterized schemes can simulate broadband snow surface albedo but cannot accurately simulate snow surface spectral reflectance with continuous and fine spectral wavebands, which constitute the major observations of current satellite sensors; consequently, there is an obvious gap between land surface model simulations and remote sensing observations. Here, we suggest a new integrated scheme that couples a radiative transfer model with a land surface model to simulate high spectral resolution snow surface reflectance information specifically targeting multisource satellite remote sensing observations. Our results indicate that the new integrated model can accurately simulate snow surface reflectance information over a large spatial scale and continuous time series. The integrated model extends the range of snow spectral reflectance simulation to the whole shortwave band and can predict snow spectral reflectance changes in the solar spectrum region based on meteorological element data. The kappa coefficients (K) of both the narrowband snow albedo targeting Moderate Resolution Imaging Spectroradiometer (MODIS) data simulated by the new integrated model and the retrieved snow albedo based on MODIS reflectance data are 0.5, and both exhibit good spatial consistency. Our proposed narrowband snow albedo simulation scheme targeting satellite remote sensing observations is consistent with remote sensing satellite observations in time series and can predict narrowband snow albedo even during periods of missing remote sensing observations. This new integrated model is a significant improvement over traditional land surface models for the direct spectral observations of satellite remote sensing. The proposed model could contribute to the effective combination of snow surface reflectance information from multisource remote sensing observations with land surface models.